Multi-parameters optimization for electromagnetic acoustic transducers using surrogate-assisted particle swarm optimizer

Jia, Xiaojuan; Liang, Jing; Zhao, Kai; Yang, Zhile; Yu, Ming-yuan

doi:10.1016/j.ymssp.2020.107337

Cited by 17 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, the material used as the specimen is aluminum plate, which is a paramagnetic material. Therefore, the main focus of this paper, the Lorentz force mechanism, is assumed the dominant mechanism; this assumption is sound base on [2,3,16,24]. Other mechanisms, such as magnetization and magnetostrictive force mechanisms, are ignored.…”

Section: Theoretical Considerationsmentioning

confidence: 99%

“…The NDT&E methods include ultrasonic testing (UT), magnetic flux leaking (MFL), alternating current field measurement (ACMF), eddy current testing (ECT), etc. [1][2][3][4][5]. Among the NDT&E methods mentioned above, electromagnetic ultrasonic transducer (EMAT) as a nonconduct UT approach is widely used for thickness measurement and material characterization due to its ability to generate and receive ultrasonic waves without physical contact or coupling with the surface of the workpiece being inspected.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the Influence of Design Frequency on the Ultrasonic Signal of PFSV-EMATs

et al. 2022

View full text Add to dashboard Cite

The point-focusing shear vertical wave electromagnetic acoustic transducer (PFSV-EMAT) with a concentric meander line (CML) coil has been developed to detect and size micro defection concealed within a certain depth of materials. It is proved to be sensitive to the design frequency, which is associated with the spacing of CML coil. In this work, the relationship between the design frequency and characteristic of PFSV-EMATs, such as the particle displacement and focal offset, is studied quantitatively. First, the configuration and working principle of PFSV-EMATs are introduced comprehensively. Then, a finite element model is established to simulate the incentive and propagation process of ultrasonic waves. In addition, the relationship between magnet-to-coil radius ratio and the signal amplitude generated by the PFSV-EMATs is explored to ensure the consistency of transducer parameters at different frequencies. Furthermore, under the optimal magnet-to-coil radius ratio, the influence of design frequency on the characteristic of PFSV-EMATs is studied. Finally, the experimental results show good agreement with the simulation results and indicate that the signal amplitude and point-focusing performance of PFSV-EMATs employing CML coils could be improved by carefully choosing the design frequency.

show abstract

Section: Theoretical Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the Influence of Design Frequency on the Ultrasonic Signal of PFSV-EMATs

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Particle swarm optimization (PSO) is a metaheuristic, stochastic, and populationbased evolutionary optimization algorithm, and its standard form was initially developed by Kennedy Eberhart [22]. It searches for an optimal solution in its search space through the modeling of a swarm, where each particle in the swarm survives with a velocity and a position in the solution search space.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm

Horng¹,

Liu²,

Sun³

et al. 2021

Preprint

View full text Add to dashboard Cite

High-strength low-alloy steels (HSLAs) are widely used in the structural body components of many domestic motor vehicles owing to their better mechanical properties and greater resistance. The real production process of HSLA steelmaking can be regarded as a model that builds on the relationship between process parameters and product quality attributes. A surrogate modeling method is used, and the resulting production process model can be applied to predict the optimal manufacturing process parameters. We used different methods in this paper to build such a surrogate model, including linear regression, random forests, support vector regression, multilayer perception, and a simplified VGG model. We then applied three bio-inspired search algorithms, namely particle swarm optimization, the artificial bee colony algorithm, and the firefly algorithm, to search for the optimal controllable manufacturing process parameters. Through experiments on 9,000 test samples used for building the surrogate model, and 299 test samples for making the optimal process parameter selection, we found that the combination of a simplified VGG model and the firefly algorithm was the most successful at reaching a success rate of 100%—in other words, when the product quality attributes of all test samples satisfy the mechanical requirements of the end products.

show abstract

“…More importantly, many types of surrogate models exist and include different kernel functions. Thus, it is difficult for a single user to have complete knowledge in the design of complex multidisciplinary systems (Jia et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

A dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm for complex system design optimization

You

Zhang

Niu

2022

View full text Add to dashboard Cite

PurposeSurrogate-assisted evolutionary algorithms (SAEAs) are the most popular algorithms used to solve design optimization problems of expensive and complex engineering systems. However, it is difficult for fixed surrogate models to maintain their accuracy and efficiency in the face of different issues. Therefore, the selection of an appropriate surrogate model remains a significant challenge. This paper aims to propose a dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm (AHSM-PSO) to address this issue.Design/methodology/approachA dynamic adaptive hybrid selection method (AHSM) is proposed. This method can identify multiple ensemble models formed by integrating different numbers of excellent individual surrogate models. Then, according to the minimum root-mean-square error, the best suitable surrogate model is dynamically selected in each generation and is used to assist PSO.FindingsExperimental studies on commonly used benchmark problems, and two real-world design optimization problems demonstrate that, compared with existing algorithms, the proposed algorithm achieves better performance.Originality/valueThe main contribution of this work is the proposal of a dynamic adaptive hybrid selection method (AHSM). This method uses the advantages of different surrogate models and eliminates the shortcomings of experience selection. Furthermore, the empirical results of the comparison of the proposed algorithm (AHSM-PSO) with existing algorithms on commonly used benchmark problems, and two real-world design optimization problems demonstrate its competitiveness.

show abstract

Multi-parameters optimization for electromagnetic acoustic transducers using surrogate-assisted particle swarm optimizer

Cited by 17 publications

References 44 publications

Investigating the Influence of Design Frequency on the Ultrasonic Signal of PFSV-EMATs

Investigating the Influence of Design Frequency on the Ultrasonic Signal of PFSV-EMATs

The Steelmaking Process Parameter Optimization with a Surrogate Model Based on Convolutional Neural Networks and the Firefly Algorithm

A dynamic adaptive hybrid surrogate-assisted particle swarm optimization algorithm for complex system design optimization

Contact Info

Product

Resources

About